| Literature DB >> 32977303 |
Shohei Nagata1, Tomoki Nakaya2, Tomoya Hanibuchi3, Shiho Amagasa4, Hiroyuki Kikuchi5, Shigeru Inoue6.
Abstract
Although the pedestrian-friendly qualities of streetscapes promote walking, quantitative understanding of streetscape functionality remains insufficient. This study proposed a novel automated method to assess streetscape walkability (SW) using semantic segmentation and statistical modeling on Google Street View images. Using compositions of segmented streetscape elements, such as buildings and street trees, a regression-style model was built to predict SW, scored using a human-based auditing method. Older female active leisure walkers living in Bunkyo Ward, Tokyo, are associated with SW scores estimated by the model (OR = 3.783; 95% CI = 1.459 to 10.409), but male walkers are not.Entities:
Keywords: Deep learning; Google street view; Neighborhood walkability; Semantic segmentation; Walking behavior
Year: 2020 PMID: 32977303 DOI: 10.1016/j.healthplace.2020.102428
Source DB: PubMed Journal: Health Place ISSN: 1353-8292 Impact factor: 4.078